It focuses on weak planetary signals — so feeble and numerous it would take humans ages to examine.

While machine learning has been used before in the search for planets beyond our solar system, it is believed to be the first time an artificial neural network like this has been used to find a new world.

"This is a really exciting discovery, and we consider it to be a successful proof of concept to be using neural networks to identify planets, even in challenging situations where the signals are very weak," said Christopher Shallue, senior software engineer at Google in Mountain View, California.

Neither NASA nor Google expect to put astronomers out of business.

Mr Shallue sees this as a tool to help astronomers have more impact and increase their productivity.

"It certainly will not replace them at all," he said.

How Google crunched data that helped NASA

The research by Google and the University of Texas at Austin, that used data from NASA, raised the prospects of new insights into the universe by feeding data into computer programs that can churn through information faster and more in-depth than humanly possible, a technique known as machine learning.

In this case, software learned differences between planets and other objects by analysing thousands of data points, achieving 96 per cent accuracy, NASA said at a news conference.

The data came from the Kepler telescope, which NASA launched into space in 2009 as part of a planet-finding mission that is expected to end next year as the spacecraft runs out of fuel.